system

Abstract

The system according to the embodiment aims to support people who have no writing experience so that they can write documents with confidence. [Solution] The system according to the embodiment includes a reception unit, a collection unit, a generation unit, a section, and a confirmation unit. The reception unit receives input of the theme and content to be written. The collection unit collects related information based on the theme and content received by the reception unit. The generation unit generates an outline for writing based on the information collected by the collection unit. The section generates specific sentences based on the outline generated by the generation unit. The confirmation unit performs a final check of the sentences generated by the generation unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that people with no writing experience feel anxious when writing text and often decline to write.

[0005] The system according to the embodiment aims to support people who have no writing experience so that they can write documents with confidence. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, a generation unit, a section, and a confirmation unit. The reception unit receives input of the theme and content to be written. The collection unit collects related information based on the theme and content received by the reception unit. The generation unit generates an outline for writing based on the information collected by the collection unit. The section generates specific sentences based on the outline generated by the generation unit. The confirmation unit performs a final check of the sentences generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can support people who have no writing experience so that they can write documents with confidence. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A writing support system according to an embodiment of the present invention uses AI to support celebrities and experts in their writing activities. The writing support system allows commentators to input the topic and content they want to write about, and AI collects related information, generates an outline, creates specific sentences, and performs a final check. This allows even commentators without writing experience to write with confidence. For example, in a writing support system, a commentator inputs the topic and content they want to write about. For example, the commentator may input a topic such as "I want to comment on the latest news." This information is then input into the AI. The AI ​​then collects related information based on the input topic. The AI ​​then collects online news articles and expert opinions to generate an outline for writing. For example, an outline such as "Major topics related to the latest news" is generated. The commentator then reviews the generated outline and makes revisions as necessary. For example, the commentator may make revisions such as "This topic is important, so I would like to write in more detail." Once revisions are complete, the AI ​​generates specific sentences. The AI ​​then creates specific sentences based on the generated outline. For example, a sentence such as "I would like to write the following commentary on the latest news." Finally, the commentator performs a final check. The generated text is reviewed and edited as necessary. This allows commentators with no writing experience to write with confidence. The writing support system can support celebrities and experts in their writing activities and reduce their anxiety about writing. In addition, the AI ​​collects relevant information and generates outlines and specific sentences, improving writing efficiency and reducing the burden on commentators.

[0029] A writing support system according to an embodiment includes a receiving unit, a collecting unit, a generating unit, and a verifying unit. The receiving unit receives input of a topic or content to be written. For example, a commentator can input a topic such as "I would like to comment on the latest news." The collecting unit collects related information based on the topic or content received by the receiving unit. For example, the collecting unit collects news articles and expert opinions from the Internet. The generating unit generates an outline for writing based on the collected information. For example, the generating unit generates an outline such as "Major topics related to the latest news." The generating unit generates specific sentences based on the generated outline. For example, the generating unit generates sentences such as "I will write the following commentary on the latest news." The verifying unit performs a final check of the generated sentences and makes corrections as necessary. For example, the verifying unit can review the generated sentences and make corrections such as "I would like to write this part in more detail." This allows the writing support system according to an embodiment to allow even commentators with no writing experience to write with confidence.

[0030] The collection unit can collect related information on news articles or expert opinions on the Internet. For example, the collection unit collects news articles from reliable news sites. For example, the collection unit can collect the latest news articles from specific news sites. The collection unit can also collect expert opinions such as expert interview articles and academic papers. For example, the collection unit can collect expert interview articles and provide them as reference information for writing. This allows for efficient collection of related information.

[0031] The generation unit can generate an outline for writing based on the collected information. The generation unit generates an outline for writing based on, for example, collected news articles and expert opinions. For example, the generation unit can generate an outline such as "major topics related to the latest news." The generation unit can also generate a specific structure for the outline, such as how to divide the outline into chapters and sections. For example, the generation unit can set chapters and sections based on the content of news articles and generate an outline. This allows for efficient generation of an outline for writing.

[0032] The generation unit can generate specific sentences based on the generated outline. The generation unit generates specific sentences based on the generated outline, for example. For example, the generation unit can generate sentences such as "Write the following comment on the latest news." The generation unit can also generate sentences taking into consideration grammar checks and consistency of content. For example, the generation unit can generate grammatically correct sentences while maintaining consistency of content. This allows for efficient generation of specific sentences.

[0033] The verification unit can perform a final check of the generated text and make corrections as necessary. For example, the verification unit can check the generated text and check for typos and omissions and the consistency of the content. For example, the verification unit can read the generated text and correct typos. The verification unit can also check the consistency of the content and make corrections as necessary. For example, the verification unit can check whether the content of the text is consistent and make corrections as necessary. In this way, the generated text can be performed a final check and make corrections as necessary.

[0034] The reception unit can analyze the user's past input history and suggest an optimal input format. The reception unit, for example, can automatically display themes and content that the user has frequently input in the past as candidates. For example, the reception unit can analyze the user's past input history and automatically display themes and content that the user has frequently input as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can preferentially suggest input methods that the user has used in the past, such as voice input or text input, based on the user's past input history. Furthermore, the reception unit can predict and suggest themes and content that will be used in a specific time period based on the user's past input history. For example, the reception unit can analyze the user's past input history and predict and suggest themes and content that will be frequently used in a specific time period. This improves input efficiency by suggesting an optimal input format based on the past input history.

[0035] When inputting a theme or content, the reception unit can present input candidates based on the user's current interests and trends. The reception unit can suggest related themes and content based on, for example, keywords recently searched by the user or articles recently viewed by the user. For example, the reception unit can analyze the user's recent search history or browsing history to suggest related themes and content. The reception unit can also analyze the user's social media activities and present input candidates based on the user's current interests. For example, the reception unit can analyze the user's social media activities and present input candidates based on the user's current interests. Furthermore, the reception unit can suggest themes and content that the user is likely to be interested in based on the latest news and trend information. For example, the reception unit can collect the latest news and trend information and suggest themes and content that the user is likely to be interested in. This allows the user to input more appropriate themes and content by presenting input candidates based on the user's interests and trends.

[0036] When inputting a theme or content, the reception unit can select an optimal input means according to the user's input method. For example, if the user selects voice input, the reception unit can input the theme or content using voice recognition technology. For example, if the user selects voice input, the reception unit can input the theme or content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can input the theme or content using a keyboard or a touch screen. For example, if the user selects text input, the reception unit can input the theme or content using a keyboard or a touch screen. Furthermore, if the user selects image input, the reception unit can input the theme or content using image recognition technology. For example, if the user selects image input, the reception unit can input the theme or content using image recognition technology. This improves input efficiency by selecting an optimal input means according to the user's input method.

[0037] When inputting a theme or content, the reception unit can prioritize the presentation of highly relevant themes based on the user's geographical location information. For example, if the user is in a specific region, the reception unit can suggest news and topics related to the region. For example, if the reception unit estimates that the user is in a specific region, the reception unit can suggest news and topics related to the region. Furthermore, if the user is traveling, the reception unit can suggest themes and content related to the travel destination. For example, if the reception unit estimates that the user is traveling, the reception unit can suggest themes and content related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can suggest themes and content related to the event. For example, if the reception unit estimates that the user is participating in a specific event, the reception unit can suggest themes and content related to the event. This allows the user to input more appropriate themes and content by presenting highly relevant themes in consideration of the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity and suggest related themes when the user inputs a theme or content. The reception unit can, for example, suggest related themes or content based on topics frequently mentioned by the user on social media. For example, the reception unit can analyze the user's social media activity and suggest related themes or content based on the frequently mentioned topics. The reception unit can also suggest related themes or content by referring to the activity of the user's friends on social media. For example, the reception unit can suggest related themes or content based on topics mentioned by the user's friends on social media. Furthermore, the reception unit can analyze the content of the user's posts on social media and suggest related themes or content. For example, the reception unit can analyze the content of the user's posts and suggest related themes or content. In this way, by analyzing the user's social media activity and suggesting related themes, more appropriate themes and content can be input.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when inputting a theme or content. The reception unit, for example, suggests an optimal input method based on feedback provided by the user in the past. For example, the reception unit can analyze the user's past feedback and suggest an optimal input method. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. For example, the reception unit can preferentially suggest a specific input method, such as voice input or text input, based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the input method. For example, the reception unit can customize the layout of the input form and the input procedure based on the user's past feedback. In this way, customizing the input method by reflecting the user's past feedback enables more appropriate input.

[0040] The collection unit can evaluate the reliability of information at the time of collection and preferentially collect highly reliable information. The collection unit, for example, preferentially collects highly reliable news sites and expert opinions. For example, the collection unit can preferentially collect specific news sites and expert opinions as highly reliable information sources. The collection unit can also evaluate the source of the information and exclude low reliability information. For example, the collection unit can evaluate the source of the information and exclude low reliability information. Furthermore, the collection unit can compare information from multiple information sources and select highly reliable information. For example, the collection unit can compare information from multiple information sources and select highly reliable information. In this way, by evaluating the reliability of information and preferentially collecting highly reliable information, more accurate information can be provided.

[0041] The collection unit may apply different collection algorithms depending on the category of information when collecting the information. For example, in the case of news articles, the collection unit may apply an algorithm that prioritizes collecting the latest information. For example, when collecting news articles, the collection unit may apply an algorithm that prioritizes collecting the latest information. Furthermore, in the case of expert opinions, the collection unit may apply an algorithm that prioritizes collecting reliable information sources. For example, when collecting expert opinions, the collection unit may apply an algorithm that prioritizes collecting reliable information sources. Furthermore, in the case of social media posts, the collection unit may apply an algorithm that collects information based on the user's interests. For example, when collecting social media posts, the collection unit may apply an algorithm that collects information based on the user's interests. In this way, by applying different collection algorithms depending on the category of information, more appropriate information can be collected.

[0042] The collection unit can improve the accuracy of collection by referring to the user's past collection history when collecting data. The collection unit, for example, preferentially collects related information based on information collected by the user in the past. For example, the collection unit can analyze the user's past collection history and preferentially collect related information. The collection unit can also preferentially collect specific information sources from the user's past collection history. For example, the collection unit can preferentially collect specific information sources based on the user's past collection history. Furthermore, the collection unit can analyze the user's past collection history and optimize the collection algorithm. For example, the collection unit can optimize the collection algorithm based on the user's past collection history and improve the accuracy of collection. In this way, more appropriate information can be collected by improving the accuracy of collection by referring to the user's past collection history.

[0043] The collection unit can determine the priority of collection based on the time of submission of information at the time of collection. The collection unit, for example, prioritizes collection of the latest news articles. For example, the collection unit can prioritize collection of the latest news articles. The collection unit can also prioritize collection of information whose submission deadline is approaching. For example, the collection unit can prioritize collection of information whose submission deadline is approaching. Furthermore, the collection unit can also prioritize collection of information related to a time period specified by the user. For example, the collection unit can prioritize collection of information related to a time period specified by the user. In this way, by determining the priority of collection based on the time of submission of information, more appropriate information can be collected.

[0044] The collection unit can adjust the order of collection based on the relevance of information during collection. The collection unit, for example, prioritizes collection of information related to a theme specified by the user. For example, the collection unit can prioritize collection of information related to a theme specified by the user. The collection unit can also prioritize collection of highly relevant information based on the user's interests. For example, the collection unit can prioritize collection of highly relevant information based on the user's interests. Furthermore, the collection unit can also prioritize collection of highly relevant information based on the user's past collection history. For example, the collection unit can prioritize collection of highly relevant information based on the user's past collection history. In this way, by adjusting the order of collection based on the relevance of information, more appropriate information can be collected.

[0045] The collection unit can adjust the level of detail of the information to be collected according to the user's level of expertise during collection. For example, if the user is an expert, the collection unit can prioritize collecting detailed information. For example, if the collection unit estimates that the user is an expert, the collection unit can prioritize collecting detailed information. Furthermore, if the user is a beginner, the collection unit can prioritize collecting basic information. For example, if the collection unit estimates that the user is a beginner, the collection unit can prioritize collecting basic information. Furthermore, the collection unit can adjust the level of detail of the information according to the user's level of expertise. For example, the collection unit can adjust the level of detail of the information, such as by using technical terms or adding detailed explanations, according to the user's level of expertise. As a result, by adjusting the level of detail of the information according to the user's level of expertise, more appropriate information can be collected.

[0046] The generation unit can adjust the level of detail of the outline and the sentences based on the importance of the information during generation. For example, the generation unit can generate detailed outlines and sentences for important information. For example, the generation unit can generate detailed outlines and sentences for important information. The generation unit can also generate concise outlines and sentences for less important information. For example, the generation unit can generate concise outlines and sentences for less important information. Furthermore, the generation unit can adjust the level of detail of the outline and the sentences based on the importance of the information. For example, the generation unit can adjust the level of detail of the outline and the sentences, such as by adding detailed explanations or omitting information, based on the importance of the information. In this way, by adjusting the level of detail of the outline and the sentences based on the importance of the information, more appropriate information can be provided.

[0047] The generation unit may apply different generation algorithms depending on the category of information during generation. For example, in the case of a news article, the generation unit may apply an algorithm that generates an outline and sentences based on the latest information. For example, when generating a news article, the generation unit may apply an algorithm that generates an outline and sentences based on the latest information. Furthermore, in the case of an expert's opinion, the generation unit may apply an algorithm that generates an outline and sentences based on reliable information. For example, when generating an expert's opinion, the generation unit may apply an algorithm that generates an outline and sentences based on reliable information. Furthermore, in the case of a social media post, the generation unit may apply an algorithm that generates an outline and sentences based on a user's interests. For example, when generating a social media post, the generation unit may apply an algorithm that generates an outline and sentences based on a user's interests. In this way, by applying different generation algorithms depending on the category of information, more appropriate information can be provided.

[0048] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, preferentially generates related information based on outlines and sentences generated by the user in the past. For example, the generation unit can analyze the user's past generation results and preferentially generate related information. The generation unit can also preferentially apply a specific generation algorithm based on the user's past generation results. For example, the generation unit can preferentially apply a specific generation algorithm based on the user's past generation results. Furthermore, the generation unit can analyze the user's past generation results and optimize the generation algorithm. For example, the generation unit can optimize the generation algorithm based on the user's past generation results and improve the accuracy of generation. In this way, by improving the accuracy of generation by referring to the user's past generation results, more appropriate information can be provided.

[0049] At the time of generation, the generation unit can determine the priority of the outline and the text based on the time of submission of the information. For example, the generation unit can preferentially reflect information with an upcoming submission deadline in the outline and the text. For example, the generation unit can preferentially reflect information with an upcoming submission deadline in the outline and the text. The generation unit can also preferentially reflect information related to a time period specified by the user in the outline and the text. For example, the generation unit can preferentially reflect information related to a time period specified by the user in the outline and the text. Furthermore, the generation unit can also preferentially reflect the latest news articles in the outline and the text. For example, the generation unit can preferentially reflect the latest news articles in the outline and the text. In this way, by determining the priority of the outline and the text based on the time of submission of the information, more appropriate information can be provided.

[0050] The generation unit can adjust the order of the outline and the sentences based on the relevance of the information during generation. For example, the generation unit can prioritize information related to a theme specified by the user in the outline and the sentences. For example, the generation unit can prioritize information related to a theme specified by the user in the outline and the sentences. The generation unit can also prioritize information that is highly relevant in the outline and the sentences based on the user's interests. For example, the generation unit can prioritize information that is highly relevant in the outline and the sentences based on the user's interests. Furthermore, the generation unit can also prioritize information that is highly relevant in the outline and the sentences based on the user's past generation history. For example, the generation unit can prioritize information that is highly relevant in the outline and the sentences based on the user's past generation history. In this way, by adjusting the order of the outline and the sentences based on the relevance of the information, more appropriate information can be provided.

[0051] The generation unit may adjust the use of technical terms in the outline and the text during generation according to the user's level of expertise. For example, if the user is an expert, the generation unit may generate an outline and text that use a lot of technical terms. For example, if the generation unit estimates that the user is an expert, the generation unit may generate an outline and text that use a lot of technical terms. Furthermore, if the user is a beginner, the generation unit may generate an outline and text that use basic terms. For example, if the generation unit estimates that the user is a beginner, the generation unit may generate an outline and text that use basic terms. Furthermore, the generation unit may adjust the use of technical terms in the outline and the text according to the user's level of expertise. For example, the generation unit may adjust the use of technical terms in the outline and the text, such as by defining technical terms or replacing them with general terms, according to the user's level of expertise. This allows for more appropriate information to be provided by adjusting the use of technical terms according to the user's level of expertise.

[0052] The confirmation unit can select the optimal confirmation method by referring to the user's past confirmation history when confirming. The confirmation unit can, for example, suggest the optimal confirmation method based on confirmation methods used by the user in the past. For example, the confirmation unit can analyze the user's past confirmation history and suggest the optimal confirmation method. The confirmation unit can also preferentially suggest a specific confirmation method based on the user's past confirmation history. For example, the confirmation unit can preferentially suggest a specific confirmation method based on the user's past confirmation history. Furthermore, the confirmation unit can analyze the user's past confirmation history and optimize the confirmation method. For example, the confirmation unit can optimize the confirmation method based on the user's past confirmation history and improve the accuracy of confirmation. This enables more appropriate confirmation by selecting the optimal confirmation method by referring to the user's past confirmation history.

[0053] The confirmation unit can customize the confirmation content according to the user's current task at the time of confirmation. For example, if the user is writing, the confirmation unit prioritizes checking information related to writing. For example, if the confirmation unit estimates that the user is writing, it can prioritize checking information related to writing. Furthermore, if the user is doing research, the confirmation unit can prioritize checking information related to research. For example, if the confirmation unit estimates that the user is doing research, it can prioritize checking information related to research. Furthermore, the confirmation unit can customize the confirmation content according to the user's current task. For example, the confirmation unit can customize the confirmation content, such as adding confirmation items or changing the confirmation procedure, according to the user's current task. This allows for more appropriate confirmation by customizing the confirmation content according to the user's current task.

[0054] The confirmation unit can improve the confirmation method by reflecting user feedback during confirmation. The confirmation unit can improve the confirmation method, for example, based on feedback provided by the user. For example, the confirmation unit can analyze the user's feedback and improve the confirmation method. The confirmation unit can also preferentially suggest a specific confirmation method based on the user's past feedback. For example, the confirmation unit can preferentially suggest a specific confirmation method based on the user's past feedback. Furthermore, the confirmation unit can analyze the user's feedback and optimize the confirmation method. For example, the confirmation unit can optimize the confirmation method based on the user's feedback and improve the accuracy of confirmation. As a result, improving the confirmation method by reflecting the user's feedback enables more appropriate confirmation.

[0055] The confirmation unit can select an optimal confirmation method based on the user's device information at the time of confirmation. For example, if the user is using a smartphone, the confirmation unit can provide a confirmation method tailored to the screen size. For example, if the confirmation unit estimates that the user is using a smartphone, the confirmation unit can provide a confirmation method tailored to the screen size. Furthermore, if the user is using a tablet, the confirmation unit can provide a confirmation method optimized for a large screen. For example, if the confirmation unit estimates that the user is using a tablet, the confirmation unit can provide a confirmation method optimized for a large screen. Furthermore, if the user is using a smartwatch, the confirmation unit can provide a simple and highly visible confirmation method. For example, if the confirmation unit estimates that the user is using a smartwatch, the confirmation unit can provide a simple and highly visible confirmation method. This enables more appropriate confirmation by selecting the optimal confirmation method in consideration of the user's device information.

[0056] The confirmation unit can make the confirmation content multilingual in accordance with the user's language setting at the time of confirmation. The confirmation unit, for example, automatically sets the confirmation content based on the language setting of the user's device. For example, the confirmation unit can automatically set the confirmation content based on the language setting of the user's device. The confirmation unit can also provide a language switching function when the user uses multiple languages. For example, the confirmation unit can provide a language switching function when the user uses multiple languages. Furthermore, the confirmation unit can provide the confirmation content in a specific language when the user selects that language. For example, the confirmation unit can provide the confirmation content in that language when the user selects that language. This makes the confirmation content multilingual in accordance with the user's language setting, thereby enabling more appropriate confirmation.

[0057] The confirmation unit can customize the confirmation method by reflecting the user's past feedback when confirming. The confirmation unit can propose an optimal confirmation method based on, for example, feedback provided by the user in the past. For example, the confirmation unit can analyze the user's past feedback and propose an optimal confirmation method. The confirmation unit can also preferentially propose a specific confirmation method based on the user's past feedback. For example, the confirmation unit can preferentially propose a specific confirmation method based on the user's past feedback. Furthermore, the confirmation unit can analyze the user's past feedback and customize the confirmation method. For example, the confirmation unit can customize the confirmation method by changing the confirmation procedure or adjusting the confirmation content based on the user's past feedback. In this way, customizing the confirmation method by reflecting the user's past feedback enables more appropriate confirmation.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can automatically search for related past writing data based on the user's input and provide it as reference information. For example, if the user inputs "I want to comment on the latest news," the reception unit can search for articles and comments written in the past on a similar topic and provide it as reference information. Furthermore, if the user inputs a specific keyword, the reception unit can preferentially display past writing data related to the keyword. For example, if the keyword "environmental issues" is input, the reception unit can preferentially display articles written in the past about environmental issues. Furthermore, the reception unit can analyze the user's past writing history and automatically suggest related past writing data. For example, the reception unit can analyze the themes and contents of articles written by the user in the past and suggest related past writing data. This allows the user to write more efficiently by referring to past writing data.

[0060] The collection unit can automatically suggest new related information based on the user's past collection history. For example, the collection unit can analyze news articles and expert opinions that the user has collected in the past and suggest new related information. The collection unit can also analyze trends in information that the user has collected in the past and preferentially collect new information on similar themes or content. For example, if the user has frequently collected information on environmental issues in the past, the collection unit can preferentially collect information on the latest environmental issues. Furthermore, the collection unit can automatically suggest new information from a specific information source based on the user's past collection history. For example, if the user has frequently collected information from a specific news site in the past, the collection unit can preferentially suggest new information from that news site. This enables more efficient information collection by suggesting new related information based on the user's past collection history.

[0061] The generation unit can customize the style and tone of the text to be generated based on the user's past generation results. For example, the generation unit can analyze the style and tone of text generated by the user in the past and generate new text in a similar style and tone. The generation unit can also analyze the trends of text generated by the user in the past and generate text in a style and tone that matches the user's preferences. For example, if the user has generated text in a formal style in the past, the generation unit can generate new text in a similar formal style. Furthermore, the generation unit can customize the style and tone for a specific theme or content based on the user's past generation results. For example, if the user has generated text on environmental issues in the past, the generation unit can refer to the past style and tone when generating new text on a similar theme. In this way, by customizing the style and tone based on the user's past generation results, more appropriate text can be provided.

[0062] The confirmation unit can optimize the confirmation procedure and content based on the user's past confirmation history. For example, the confirmation unit can analyze the confirmation procedure used by the user in the past and suggest an optimal procedure. The confirmation unit can also optimize the confirmation procedure for similar content based on content that the user has confirmed in the past. For example, if the user has confirmed content related to a specific topic in the past, the confirmation unit can refer to the past procedure when confirming new content related to that topic. Furthermore, the confirmation unit can preferentially suggest a specific confirmation method based on the user's past confirmation history. For example, if the user has frequently used voice confirmation in the past, the confirmation unit can preferentially suggest voice confirmation. This enables more efficient confirmation by optimizing the confirmation procedure and content based on the user's past confirmation history.

[0063] The collection unit may apply different collection strategies depending on the category of information to be collected. For example, in the case of news articles, the collection unit may apply a strategy of preferentially collecting the latest information. For example, when collecting news articles, the collection unit may apply a strategy of preferentially collecting the latest information. Furthermore, in the case of expert opinions, the collection unit may apply a strategy of preferentially collecting reliable sources. For example, when collecting expert opinions, the collection unit may apply a strategy of preferentially collecting reliable sources. Furthermore, in the case of social media posts, the collection unit may apply a strategy of collecting information based on user interests. For example, when collecting social media posts, the collection unit may apply a strategy of collecting information based on user interests. In this way, by applying different collection strategies depending on the category of information, more appropriate information can be collected.

[0064] The generation unit can apply different generation algorithms depending on the content of the sentence to be generated. For example, in the case of a news article, the generation unit can apply an algorithm that generates sentences based on the latest information. For example, when generating a news article, the generation unit can apply an algorithm that generates sentences based on the latest information. Furthermore, in the case of an expert's opinion, the generation unit can apply an algorithm that generates sentences based on reliable information. For example, when generating an expert's opinion, the generation unit can apply an algorithm that generates sentences based on reliable information. Furthermore, in the case of a social media post, the generation unit can apply an algorithm that generates sentences based on a user's interests. For example, when generating a social media post, the generation unit can apply an algorithm that generates sentences based on a user's interests. In this way, by applying different generation algorithms depending on the content of the sentence to be generated, more appropriate sentences can be provided.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception section accepts input of the topic and content that the commentator wants to write about. For example, a commentator can input a topic such as "I want to comment on the latest news." Step 2: The collection unit collects related information based on the themes and content received by the reception unit. For example, the collection unit collects news articles and expert opinions from the Internet. Step 3: The generator generates an outline for writing based on the collected information. For example, the generator generates an outline such as "Major topics in the latest news." Step 4: The generator generates a specific sentence based on the generated outline. For example, the generator generates a sentence such as "Write the following comment about the latest news." Step 5: The verification section performs a final check of the generated text and makes corrections as necessary. For example, the verification section can review the generated text and make corrections such as "I would like to write this part in more detail."

[0067] (Example 2) A writing support system according to an embodiment of the present invention uses AI to support celebrities and experts in their writing activities. The writing support system allows commentators to input the topic and content they want to write about, and AI collects related information, generates an outline, creates specific sentences, and performs a final check. This allows even commentators without writing experience to write with confidence. For example, in a writing support system, a commentator inputs the topic and content they want to write about. For example, the commentator may input a topic such as "I want to comment on the latest news." This information is then input into the AI. The AI ​​then collects related information based on the input topic. The AI ​​then collects online news articles and expert opinions to generate an outline for writing. For example, an outline such as "Major topics related to the latest news" is generated. The commentator then reviews the generated outline and makes revisions as necessary. For example, the commentator may make revisions such as "This topic is important, so I would like to write in more detail." Once revisions are complete, the AI ​​generates specific sentences. The AI ​​then creates specific sentences based on the generated outline. For example, a sentence such as "I would like to write the following commentary on the latest news." Finally, the commentator performs a final check. The generated text is reviewed and edited as necessary. This allows commentators with no writing experience to write with confidence. The writing support system can support celebrities and experts in their writing activities and reduce their anxiety about writing. In addition, the AI ​​collects relevant information and generates outlines and specific sentences, improving writing efficiency and reducing the burden on commentators.

[0068] A writing support system according to an embodiment includes a receiving unit, a collecting unit, a generating unit, and a verifying unit. The receiving unit receives input of a topic or content to be written. For example, a commentator can input a topic such as "I would like to comment on the latest news." The collecting unit collects related information based on the topic or content received by the receiving unit. For example, the collecting unit collects news articles and expert opinions from the Internet. The generating unit generates an outline for writing based on the collected information. For example, the generating unit generates an outline such as "Major topics related to the latest news." The generating unit generates specific sentences based on the generated outline. For example, the generating unit generates sentences such as "I will write the following commentary on the latest news." The verifying unit performs a final check of the generated sentences and makes corrections as necessary. For example, the verifying unit can review the generated sentences and make corrections such as "I would like to write this part in more detail." This allows the writing support system according to an embodiment to allow even commentators with no writing experience to write with confidence.

[0069] The collection unit can collect related information on news articles or expert opinions on the Internet. For example, the collection unit collects news articles from reliable news sites. For example, the collection unit can collect the latest news articles from specific news sites. The collection unit can also collect expert opinions such as expert interview articles and academic papers. For example, the collection unit can collect expert interview articles and provide them as reference information for writing. This allows for efficient collection of related information.

[0070] The generation unit can generate an outline for writing based on the collected information. The generation unit generates an outline for writing based on, for example, collected news articles and expert opinions. For example, the generation unit can generate an outline such as "major topics related to the latest news." The generation unit can also generate a specific structure for the outline, such as how to divide the outline into chapters and sections. For example, the generation unit can set chapters and sections based on the content of news articles and generate an outline. This allows for efficient generation of an outline for writing.

[0071] The generation unit can generate specific sentences based on the generated outline. The generation unit generates specific sentences based on the generated outline, for example. For example, the generation unit can generate sentences such as "Write the following comment on the latest news." The generation unit can also generate sentences taking into consideration grammar checks and consistency of content. For example, the generation unit can generate grammatically correct sentences while maintaining consistency of content. This allows for efficient generation of specific sentences.

[0072] The verification unit can perform a final check of the generated text and make corrections as necessary. For example, the verification unit can check the generated text and check for typos and omissions and the consistency of the content. For example, the verification unit can read the generated text and correct typos. The verification unit can also check the consistency of the content and make corrections as necessary. For example, the verification unit can check whether the content of the text is consistent and make corrections as necessary. In this way, the generated text can be performed a final check and make corrections as necessary.

[0073] The reception unit can estimate the user's emotions and adjust the input method for the topic or content based on the estimated emotions. For example, if the user is nervous, the reception unit can provide a simple and intuitive interface to minimize the input procedure. For example, if the reception unit estimates that the user is nervous, it can display a simple input form to simplify the input procedure. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, if the reception unit estimates that the user is relaxed, it can display detailed input options to allow the user to freely customize the input content. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to allow the user to quickly input the topic or content. For example, if the reception unit estimates that the user is in a hurry, it can prioritize voice input to allow the user to quickly input the topic or content. This allows more appropriate input by adjusting the input method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The reception unit can analyze the user's past input history and suggest an optimal input format. The reception unit, for example, can automatically display themes and content that the user has frequently input in the past as candidates. For example, the reception unit can analyze the user's past input history and automatically display themes and content that the user has frequently input as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can preferentially suggest input methods that the user has used in the past, such as voice input or text input, based on the user's past input history. Furthermore, the reception unit can predict and suggest themes and content that will be used in a specific time period based on the user's past input history. For example, the reception unit can analyze the user's past input history and predict and suggest themes and content that will be frequently used in a specific time period. This improves input efficiency by suggesting an optimal input format based on the past input history.

[0075] When inputting a theme or content, the reception unit can present input candidates based on the user's current interests and trends. The reception unit can suggest related themes and content based on, for example, keywords recently searched by the user or articles recently viewed by the user. For example, the reception unit can analyze the user's recent search history or browsing history to suggest related themes and content. The reception unit can also analyze the user's social media activities and present input candidates based on the user's current interests. For example, the reception unit can analyze the user's social media activities and present input candidates based on the user's current interests. Furthermore, the reception unit can suggest themes and content that the user is likely to be interested in based on the latest news and trend information. For example, the reception unit can collect the latest news and trend information and suggest themes and content that the user is likely to be interested in. This allows the user to input more appropriate themes and content by presenting input candidates based on the user's interests and trends.

[0076] When inputting a theme or content, the reception unit can select an optimal input means according to the user's input method. For example, if the user selects voice input, the reception unit can input the theme or content using voice recognition technology. For example, if the user selects voice input, the reception unit can input the theme or content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can input the theme or content using a keyboard or a touch screen. For example, if the user selects text input, the reception unit can input the theme or content using a keyboard or a touch screen. Furthermore, if the user selects image input, the reception unit can input the theme or content using image recognition technology. For example, if the user selects image input, the reception unit can input the theme or content using image recognition technology. This improves input efficiency by selecting an optimal input means according to the user's input method.

[0077] The reception unit can estimate the user's emotions and prioritize input content based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can suggest that the user prioritize input of important topics or content. For example, if the reception unit estimates that the user is feeling stressed, the reception unit can suggest that the user prioritize input of important topics or content. Furthermore, if the user is relaxed, the reception unit can suggest that the user prioritize input of detailed topics or content. For example, if the reception unit estimates that the user is relaxed, the reception unit can suggest that the user prioritize input of detailed topics or content. Furthermore, if the user is in a hurry, the reception unit can suggest that the user prioritize input of concise topics or content. For example, if the reception unit estimates that the user is in a hurry, the reception unit can suggest that the user prioritize input of concise topics or content. This enables more appropriate input by prioritizing input content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] When inputting a theme or content, the reception unit can prioritize the presentation of highly relevant themes based on the user's geographical location information. For example, if the user is in a specific region, the reception unit can suggest news and topics related to the region. For example, if the reception unit estimates that the user is in a specific region, the reception unit can suggest news and topics related to the region. Furthermore, if the user is traveling, the reception unit can suggest themes and content related to the travel destination. For example, if the reception unit estimates that the user is traveling, the reception unit can suggest themes and content related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can suggest themes and content related to the event. For example, if the reception unit estimates that the user is participating in a specific event, the reception unit can suggest themes and content related to the event. This allows the user to input more appropriate themes and content by presenting highly relevant themes in consideration of the user's geographical location information.

[0079] The reception unit can analyze the user's social media activity and suggest related themes when the user inputs a theme or content. The reception unit can, for example, suggest related themes or content based on topics frequently mentioned by the user on social media. For example, the reception unit can analyze the user's social media activity and suggest related themes or content based on the frequently mentioned topics. The reception unit can also suggest related themes or content by referring to the activity of the user's friends on social media. For example, the reception unit can suggest related themes or content based on topics mentioned by the user's friends on social media. Furthermore, the reception unit can analyze the content of the user's posts on social media and suggest related themes or content. For example, the reception unit can analyze the content of the user's posts and suggest related themes or content. In this way, by analyzing the user's social media activity and suggesting related themes, more appropriate themes and content can be input.

[0080] The reception unit can customize the input method by reflecting the user's past feedback when inputting a theme or content. The reception unit, for example, suggests an optimal input method based on feedback provided by the user in the past. For example, the reception unit can analyze the user's past feedback and suggest an optimal input method. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. For example, the reception unit can preferentially suggest a specific input method, such as voice input or text input, based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the input method. For example, the reception unit can customize the layout of the input form and the input procedure based on the user's past feedback. In this way, customizing the input method by reflecting the user's past feedback enables more appropriate input.

[0081] The collection unit can estimate the user's emotions and adjust the range of information to be collected based on the estimated emotions. For example, when the user is relaxed, the collection unit can collect a wide range of information and generate a detailed outline. For example, when the collection unit estimates that the user is relaxed, the collection unit can collect a wide range of information and generate a detailed outline. Furthermore, when the user is in a hurry, the collection unit can narrow down the collection to important information and generate a concise outline. For example, when the collection unit estimates that the user is in a hurry, the collection unit can narrow down the collection to important information and generate a concise outline. Furthermore, when the user is excited, the collection unit can prioritize collecting visually stimulating information. For example, when the collection unit estimates that the user is excited, the collection unit can prioritize collecting visually stimulating information. This allows for adjusting the range of information to be collected according to the user's emotions, thereby collecting more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The collection unit can evaluate the reliability of information at the time of collection and preferentially collect highly reliable information. The collection unit, for example, preferentially collects highly reliable news sites and expert opinions. For example, the collection unit can preferentially collect specific news sites and expert opinions as highly reliable information sources. The collection unit can also evaluate the source of the information and exclude low reliability information. For example, the collection unit can evaluate the source of the information and exclude low reliability information. Furthermore, the collection unit can compare information from multiple information sources and select highly reliable information. For example, the collection unit can compare information from multiple information sources and select highly reliable information. In this way, by evaluating the reliability of information and preferentially collecting highly reliable information, more accurate information can be provided.

[0083] The collection unit may apply different collection algorithms depending on the category of information when collecting the information. For example, in the case of news articles, the collection unit may apply an algorithm that prioritizes collecting the latest information. For example, when collecting news articles, the collection unit may apply an algorithm that prioritizes collecting the latest information. Furthermore, in the case of expert opinions, the collection unit may apply an algorithm that prioritizes collecting reliable information sources. For example, when collecting expert opinions, the collection unit may apply an algorithm that prioritizes collecting reliable information sources. Furthermore, in the case of social media posts, the collection unit may apply an algorithm that collects information based on the user's interests. For example, when collecting social media posts, the collection unit may apply an algorithm that collects information based on the user's interests. In this way, by applying different collection algorithms depending on the category of information, more appropriate information can be collected.

[0084] The collection unit can improve the accuracy of collection by referring to the user's past collection history when collecting data. The collection unit, for example, preferentially collects related information based on information collected by the user in the past. For example, the collection unit can analyze the user's past collection history and preferentially collect related information. The collection unit can also preferentially collect specific information sources from the user's past collection history. For example, the collection unit can preferentially collect specific information sources based on the user's past collection history. Furthermore, the collection unit can analyze the user's past collection history and optimize the collection algorithm. For example, the collection unit can optimize the collection algorithm based on the user's past collection history and improve the accuracy of collection. In this way, more appropriate information can be collected by improving the accuracy of collection by referring to the user's past collection history.

[0085] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting important information. For example, if the collection unit estimates that the user is feeling stressed, the collection unit can prioritize collecting important information. Furthermore, if the user is relaxed, the collection unit can prioritize collecting detailed information. For example, if the collection unit estimates that the user is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting concise information. For example, if the collection unit estimates that the user is in a hurry, the collection unit can prioritize collecting concise information. This allows more appropriate information to be collected by determining the priority of information to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The collection unit can determine the priority of collection based on the time of submission of information at the time of collection. The collection unit, for example, prioritizes collection of the latest news articles. For example, the collection unit can prioritize collection of the latest news articles. The collection unit can also prioritize collection of information whose submission deadline is approaching. For example, the collection unit can prioritize collection of information whose submission deadline is approaching. Furthermore, the collection unit can also prioritize collection of information related to a time period specified by the user. For example, the collection unit can prioritize collection of information related to a time period specified by the user. In this way, by determining the priority of collection based on the time of submission of information, more appropriate information can be collected.

[0087] The collection unit can adjust the order of collection based on the relevance of information during collection. The collection unit, for example, prioritizes collection of information related to a theme specified by the user. For example, the collection unit can prioritize collection of information related to a theme specified by the user. The collection unit can also prioritize collection of highly relevant information based on the user's interests. For example, the collection unit can prioritize collection of highly relevant information based on the user's interests. Furthermore, the collection unit can also prioritize collection of highly relevant information based on the user's past collection history. For example, the collection unit can prioritize collection of highly relevant information based on the user's past collection history. In this way, by adjusting the order of collection based on the relevance of information, more appropriate information can be collected.

[0088] The collection unit can adjust the level of detail of the information to be collected according to the user's level of expertise during collection. For example, if the user is an expert, the collection unit can prioritize collecting detailed information. For example, if the collection unit estimates that the user is an expert, the collection unit can prioritize collecting detailed information. Furthermore, if the user is a beginner, the collection unit can prioritize collecting basic information. For example, if the collection unit estimates that the user is a beginner, the collection unit can prioritize collecting basic information. Furthermore, the collection unit can adjust the level of detail of the information according to the user's level of expertise. For example, the collection unit can adjust the level of detail of the information, such as by using technical terms or adding detailed explanations, according to the user's level of expertise. As a result, by adjusting the level of detail of the information according to the user's level of expertise, more appropriate information can be collected.

[0089] The generation unit can estimate the user's emotions and adjust the expression method of the outline and sentences based on the estimated emotions. For example, if the user is relaxed, the generation unit can use a relaxed expression method. For example, if the generation unit estimates that the user is relaxed, the generation unit can use a relaxed expression method. Furthermore, if the user is in a hurry, the generation unit can use a concise and to-the-point expression method. For example, if the generation unit estimates that the user is in a hurry, the generation unit can use a concise and to-the-point expression method. Furthermore, if the user is excited, the generation unit can use a visually stimulating expression method. For example, if the generation unit estimates that the user is excited, the generation unit can use a visually stimulating expression method. This enables more appropriate expression by adjusting the expression method of the outline and sentences according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The generation unit can adjust the level of detail of the outline and the sentences based on the importance of the information during generation. For example, the generation unit can generate detailed outlines and sentences for important information. For example, the generation unit can generate detailed outlines and sentences for important information. The generation unit can also generate concise outlines and sentences for less important information. For example, the generation unit can generate concise outlines and sentences for less important information. Furthermore, the generation unit can adjust the level of detail of the outline and the sentences based on the importance of the information. For example, the generation unit can adjust the level of detail of the outline and the sentences, such as by adding detailed explanations or omitting information, based on the importance of the information. In this way, by adjusting the level of detail of the outline and the sentences based on the importance of the information, more appropriate information can be provided.

[0091] The generation unit may apply different generation algorithms depending on the category of information during generation. For example, in the case of a news article, the generation unit may apply an algorithm that generates an outline and sentences based on the latest information. For example, when generating a news article, the generation unit may apply an algorithm that generates an outline and sentences based on the latest information. Furthermore, in the case of an expert's opinion, the generation unit may apply an algorithm that generates an outline and sentences based on reliable information. For example, when generating an expert's opinion, the generation unit may apply an algorithm that generates an outline and sentences based on reliable information. Furthermore, in the case of a social media post, the generation unit may apply an algorithm that generates an outline and sentences based on a user's interests. For example, when generating a social media post, the generation unit may apply an algorithm that generates an outline and sentences based on a user's interests. In this way, by applying different generation algorithms depending on the category of information, more appropriate information can be provided.

[0092] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, preferentially generates related information based on outlines and sentences generated by the user in the past. For example, the generation unit can analyze the user's past generation results and preferentially generate related information. The generation unit can also preferentially apply a specific generation algorithm based on the user's past generation results. For example, the generation unit can preferentially apply a specific generation algorithm based on the user's past generation results. Furthermore, the generation unit can analyze the user's past generation results and optimize the generation algorithm. For example, the generation unit can optimize the generation algorithm based on the user's past generation results and improve the accuracy of generation. In this way, by improving the accuracy of generation by referring to the user's past generation results, more appropriate information can be provided.

[0093] The generation unit can estimate the user's emotions and adjust the length of the outline or sentences based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise outlines or sentences. For example, if the generation unit estimates that the user is in a hurry, the generation unit can generate short, concise outlines or sentences. Furthermore, if the user is relaxed, the generation unit can generate longer outlines or sentences with detailed explanations. For example, if the generation unit estimates that the user is relaxed, the generation unit can generate longer outlines or sentences with detailed explanations. Furthermore, if the user is excited, the generation unit can generate outlines or sentences with visually stimulating effects. For example, if the generation unit estimates that the user is excited, the generation unit can generate outlines or sentences with visually stimulating effects. This allows for more appropriate information to be provided by adjusting the length of the outline or sentences according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] At the time of generation, the generation unit can determine the priority of the outline and the text based on the time of submission of the information. For example, the generation unit can preferentially reflect information with an upcoming submission deadline in the outline and the text. For example, the generation unit can preferentially reflect information with an upcoming submission deadline in the outline and the text. The generation unit can also preferentially reflect information related to a time period specified by the user in the outline and the text. For example, the generation unit can preferentially reflect information related to a time period specified by the user in the outline and the text. Furthermore, the generation unit can also preferentially reflect the latest news articles in the outline and the text. For example, the generation unit can preferentially reflect the latest news articles in the outline and the text. In this way, by determining the priority of the outline and the text based on the time of submission of the information, more appropriate information can be provided.

[0095] The generation unit can adjust the order of the outline and the sentences based on the relevance of the information during generation. For example, the generation unit can prioritize information related to a theme specified by the user in the outline and the sentences. For example, the generation unit can prioritize information related to a theme specified by the user in the outline and the sentences. The generation unit can also prioritize information that is highly relevant in the outline and the sentences based on the user's interests. For example, the generation unit can prioritize information that is highly relevant in the outline and the sentences based on the user's interests. Furthermore, the generation unit can also prioritize information that is highly relevant in the outline and the sentences based on the user's past generation history. For example, the generation unit can prioritize information that is highly relevant in the outline and the sentences based on the user's past generation history. In this way, by adjusting the order of the outline and the sentences based on the relevance of the information, more appropriate information can be provided.

[0096] The generation unit may adjust the use of technical terms in the outline and the text during generation according to the user's level of expertise. For example, if the user is an expert, the generation unit may generate an outline and text that use a lot of technical terms. For example, if the generation unit estimates that the user is an expert, the generation unit may generate an outline and text that use a lot of technical terms. Furthermore, if the user is a beginner, the generation unit may generate an outline and text that use basic terms. For example, if the generation unit estimates that the user is a beginner, the generation unit may generate an outline and text that use basic terms. Furthermore, the generation unit may adjust the use of technical terms in the outline and the text according to the user's level of expertise. For example, the generation unit may adjust the use of technical terms in the outline and the text, such as by defining technical terms or replacing them with general terms, according to the user's level of expertise. This allows for more appropriate information to be provided by adjusting the use of technical terms according to the user's level of expertise.

[0097] The confirmation unit can estimate the user's emotions and adjust the confirmation method based on the estimated emotions. For example, if the user is nervous, the confirmation unit can provide a simple and highly visible confirmation method. For example, if the confirmation unit estimates that the user is nervous, the confirmation unit can provide a simple and highly visible confirmation method. Furthermore, if the user is relaxed, the confirmation unit can provide a confirmation method that includes detailed information. For example, if the confirmation unit estimates that the user is relaxed, the confirmation unit can provide a confirmation method that includes detailed information. Furthermore, if the user is in a hurry, the confirmation unit can provide a confirmation method that focuses on the main points. For example, if the confirmation unit estimates that the user is in a hurry, the confirmation unit can provide a confirmation method that focuses on the main points. This enables more appropriate confirmation by adjusting the confirmation method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The confirmation unit can select the optimal confirmation method by referring to the user's past confirmation history when confirming. The confirmation unit can, for example, suggest the optimal confirmation method based on confirmation methods used by the user in the past. For example, the confirmation unit can analyze the user's past confirmation history and suggest the optimal confirmation method. The confirmation unit can also preferentially suggest a specific confirmation method based on the user's past confirmation history. For example, the confirmation unit can preferentially suggest a specific confirmation method based on the user's past confirmation history. Furthermore, the confirmation unit can analyze the user's past confirmation history and optimize the confirmation method. For example, the confirmation unit can optimize the confirmation method based on the user's past confirmation history and improve the accuracy of confirmation. This enables more appropriate confirmation by selecting the optimal confirmation method by referring to the user's past confirmation history.

[0099] The confirmation unit can customize the confirmation content according to the user's current task at the time of confirmation. For example, if the user is writing, the confirmation unit prioritizes checking information related to writing. For example, if the confirmation unit estimates that the user is writing, it can prioritize checking information related to writing. Furthermore, if the user is doing research, the confirmation unit can prioritize checking information related to research. For example, if the confirmation unit estimates that the user is doing research, it can prioritize checking information related to research. Furthermore, the confirmation unit can customize the confirmation content according to the user's current task. For example, the confirmation unit can customize the confirmation content, such as adding confirmation items or changing the confirmation procedure, according to the user's current task. This allows for more appropriate confirmation by customizing the confirmation content according to the user's current task.

[0100] The confirmation unit can improve the confirmation method by reflecting user feedback during confirmation. The confirmation unit can improve the confirmation method, for example, based on feedback provided by the user. For example, the confirmation unit can analyze the user's feedback and improve the confirmation method. The confirmation unit can also preferentially suggest a specific confirmation method based on the user's past feedback. For example, the confirmation unit can preferentially suggest a specific confirmation method based on the user's past feedback. Furthermore, the confirmation unit can analyze the user's feedback and optimize the confirmation method. For example, the confirmation unit can optimize the confirmation method based on the user's feedback and improve the accuracy of confirmation. As a result, improving the confirmation method by reflecting the user's feedback enables more appropriate confirmation.

[0101] The confirmation unit can estimate the user's emotions and determine a confirmation priority based on the estimated emotions. For example, if the user is feeling stressed, the confirmation unit can prioritize checking important information. For example, if the confirmation unit estimates that the user is feeling stressed, the confirmation unit can prioritize checking important information. Furthermore, if the user is relaxed, the confirmation unit can prioritize checking detailed information. For example, if the confirmation unit estimates that the user is relaxed, the confirmation unit can prioritize checking detailed information. Furthermore, if the user is in a hurry, the confirmation unit can prioritize checking concise information. For example, if the confirmation unit estimates that the user is in a hurry, the confirmation unit can prioritize checking concise information. This enables more appropriate confirmation by determining the confirmation priority based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] The confirmation unit can select an optimal confirmation method based on the user's device information at the time of confirmation. For example, if the user is using a smartphone, the confirmation unit can provide a confirmation method tailored to the screen size. For example, if the confirmation unit estimates that the user is using a smartphone, the confirmation unit can provide a confirmation method tailored to the screen size. Furthermore, if the user is using a tablet, the confirmation unit can provide a confirmation method optimized for a large screen. For example, if the confirmation unit estimates that the user is using a tablet, the confirmation unit can provide a confirmation method optimized for a large screen. Furthermore, if the user is using a smartwatch, the confirmation unit can provide a simple and highly visible confirmation method. For example, if the confirmation unit estimates that the user is using a smartwatch, the confirmation unit can provide a simple and highly visible confirmation method. This enables more appropriate confirmation by selecting the optimal confirmation method in consideration of the user's device information.

[0103] The confirmation unit can make the confirmation content multilingual in accordance with the user's language setting at the time of confirmation. The confirmation unit, for example, automatically sets the confirmation content based on the language setting of the user's device. For example, the confirmation unit can automatically set the confirmation content based on the language setting of the user's device. The confirmation unit can also provide a language switching function when the user uses multiple languages. For example, the confirmation unit can provide a language switching function when the user uses multiple languages. Furthermore, the confirmation unit can provide the confirmation content in a specific language when the user selects that language. For example, the confirmation unit can provide the confirmation content in that language when the user selects that language. This makes the confirmation content multilingual in accordance with the user's language setting, thereby enabling more appropriate confirmation.

[0104] The confirmation unit can customize the confirmation method by reflecting the user's past feedback when confirming. The confirmation unit can propose an optimal confirmation method based on, for example, feedback provided by the user in the past. For example, the confirmation unit can analyze the user's past feedback and propose an optimal confirmation method. The confirmation unit can also preferentially propose a specific confirmation method based on the user's past feedback. For example, the confirmation unit can preferentially propose a specific confirmation method based on the user's past feedback. Furthermore, the confirmation unit can analyze the user's past feedback and customize the confirmation method. For example, the confirmation unit can customize the confirmation method by changing the confirmation procedure or adjusting the confirmation content based on the user's past feedback. In this way, customizing the confirmation method by reflecting the user's past feedback enables more appropriate confirmation. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, collection unit, generation unit, and confirmation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and allows the user to input the topic and content they want to write. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects news articles and expert opinions from the Internet. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an outline and specific sentences based on the collected information. The confirmation unit is realized, for example, by the control unit 46A of the smart device 14 and performs a final check of the generated sentences and makes corrections as necessary. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, collection unit, generation unit, and confirmation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and allows the user to input the topic and content they want to write. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects news articles and expert opinions from the Internet. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an outline and specific sentences based on the collected information. The confirmation unit is realized, for example, by the control unit 46A of the smart glasses 214 and performs a final check of the generated sentences and makes corrections as necessary. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, generation unit, and confirmation unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314, and allows the user to input the topic and content they want to write. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects news articles and expert opinions from the Internet. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an outline and specific sentences based on the collected information. The confirmation unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and performs a final check of the generated sentences and makes corrections as necessary. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, generation unit, and confirmation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and allows the user to input the topic and content they want to write. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects news articles and expert opinions from the Internet. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an outline and specific sentences based on the collected information. The confirmation unit is realized, for example, by the control unit 46A of the robot 414 and performs a final check of the generated sentences and makes corrections as necessary.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The reception unit can automatically search for related past writing data based on the user's input and provide it as reference information. For example, if the user inputs "I want to comment on the latest news," the reception unit can search for articles and comments written in the past on a similar topic and provide it as reference information. Furthermore, if the user inputs a specific keyword, the reception unit can preferentially display past writing data related to the keyword. For example, if the keyword "environmental issues" is input, the reception unit can preferentially display articles written in the past about environmental issues. Furthermore, the reception unit can analyze the user's past writing history and automatically suggest related past writing data. For example, the reception unit can analyze the themes and contents of articles written by the user in the past and suggest related past writing data. This allows the user to write more efficiently by referring to past writing data.

[0107] The collection unit can estimate the user's emotions and evaluate the reliability of the information to be collected based on the estimated emotions. For example, if the user is feeling anxious, the collection unit can prioritize collecting information from reliable information sources. For example, if the collection unit estimates that the user is feeling anxious, the collection unit can prioritize collecting reliable news sites and expert opinions. Furthermore, if the user is relaxed, the collection unit can collect information from a wide range of information sources and provide diverse perspectives. For example, if the collection unit estimates that the user is relaxed, the collection unit can collect information from a wide range of news sites, blogs, and social media. Furthermore, if the user is in a hurry, the collection unit can narrow down the collection to important information and provide it quickly. For example, if the collection unit estimates that the user is in a hurry, the collection unit can prioritize collecting important news articles and expert opinions and provide them quickly. This makes it possible to provide more appropriate information by evaluating the reliability of the information to be collected according to the user's emotions.

[0108] The generation unit can estimate the user's emotions and adjust the outline structure based on the estimated emotions. For example, if the user is nervous, the generation unit can generate a simple and intuitive outline. For example, if the generation unit estimates that the user is nervous, the generation unit can generate a simple outline including only major topics. Furthermore, if the user is relaxed, the generation unit can generate a detailed outline and provide specific subtopics for each topic. For example, if the generation unit estimates that the user is relaxed, the generation unit can generate a detailed outline including specific subtopics for each topic. Furthermore, if the user is in a hurry, the generation unit can generate a concise outline that focuses on the main points. For example, if the generation unit estimates that the user is in a hurry, the generation unit can generate a concise outline that focuses on the main points. This allows for more appropriate writing support by adjusting the outline structure according to the user's emotions.

[0109] The generation unit can estimate the user's emotions for the generated sentences and adjust the tone and style of the sentences based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate sentences in a soft tone. For example, if the generation unit estimates that the user is relaxed, the generation unit can generate sentences in a soft, friendly tone. Furthermore, if the user is nervous, the generation unit can generate sentences in a formal, clear tone. For example, if the generation unit estimates that the user is nervous, the generation unit can generate sentences in a formal, clear tone. Furthermore, if the user is excited, the generation unit can generate sentences in an energetic, lively tone. For example, if the generation unit estimates that the user is excited, the generation unit can generate sentences in an energetic, lively tone. In this way, by adjusting the tone and style of the sentences according to the user's emotions, more appropriate sentences can be provided.

[0110] The confirmation unit can estimate the user's emotions and adjust the confirmation procedure based on the estimated emotions. For example, if the user is nervous, the confirmation unit can provide a simple and intuitive confirmation procedure. For example, if the confirmation unit estimates that the user is nervous, the confirmation unit can provide a simple procedure that checks only the main points. Furthermore, if the user is relaxed, the confirmation unit can provide a detailed confirmation procedure that carefully checks each point. For example, if the confirmation unit estimates that the user is relaxed, the confirmation unit can provide a detailed procedure that carefully checks each point. Furthermore, if the user is in a hurry, the confirmation unit can provide a concise confirmation procedure that focuses on the main points. For example, if the confirmation unit estimates that the user is in a hurry, the confirmation unit can provide a concise confirmation procedure that focuses on the main points. This allows for more appropriate confirmation by adjusting the confirmation procedure according to the user's emotions.

[0111] The collection unit can automatically suggest new related information based on the user's past collection history. For example, the collection unit can analyze news articles and expert opinions that the user has collected in the past and suggest new related information. The collection unit can also analyze trends in information that the user has collected in the past and preferentially collect new information on similar themes or content. For example, if the user has frequently collected information on environmental issues in the past, the collection unit can preferentially collect information on the latest environmental issues. Furthermore, the collection unit can automatically suggest new information from a specific information source based on the user's past collection history. For example, if the user has frequently collected information from a specific news site in the past, the collection unit can preferentially suggest new information from that news site. This enables more efficient information collection by suggesting new related information based on the user's past collection history.

[0112] The generation unit can customize the style and tone of the text to be generated based on the user's past generation results. For example, the generation unit can analyze the style and tone of text generated by the user in the past and generate new text in a similar style and tone. The generation unit can also analyze the trends of text generated by the user in the past and generate text in a style and tone that matches the user's preferences. For example, if the user has generated text in a formal style in the past, the generation unit can generate new text in a similar formal style. Furthermore, the generation unit can customize the style and tone for a specific theme or content based on the user's past generation results. For example, if the user has generated text on environmental issues in the past, the generation unit can refer to the past style and tone when generating new text on a similar theme. In this way, by customizing the style and tone based on the user's past generation results, more appropriate text can be provided.

[0113] The confirmation unit can optimize the confirmation procedure and content based on the user's past confirmation history. For example, the confirmation unit can analyze the confirmation procedure used by the user in the past and suggest an optimal procedure. The confirmation unit can also optimize the confirmation procedure for similar content based on content that the user has confirmed in the past. For example, if the user has confirmed content related to a specific topic in the past, the confirmation unit can refer to the past procedure when confirming new content related to that topic. Furthermore, the confirmation unit can preferentially suggest a specific confirmation method based on the user's past confirmation history. For example, if the user has frequently used voice confirmation in the past, the confirmation unit can preferentially suggest voice confirmation. This enables more efficient confirmation by optimizing the confirmation procedure and content based on the user's past confirmation history.

[0114] The collection unit may apply different collection strategies depending on the category of information to be collected. For example, in the case of news articles, the collection unit may apply a strategy of preferentially collecting the latest information. For example, when collecting news articles, the collection unit may apply a strategy of preferentially collecting the latest information. Furthermore, in the case of expert opinions, the collection unit may apply a strategy of preferentially collecting reliable sources. For example, when collecting expert opinions, the collection unit may apply a strategy of preferentially collecting reliable sources. Furthermore, in the case of social media posts, the collection unit may apply a strategy of collecting information based on user interests. For example, when collecting social media posts, the collection unit may apply a strategy of collecting information based on user interests. In this way, by applying different collection strategies depending on the category of information, more appropriate information can be collected.

[0115] The generation unit can apply different generation algorithms depending on the content of the sentence to be generated. For example, in the case of a news article, the generation unit can apply an algorithm that generates sentences based on the latest information. For example, when generating a news article, the generation unit can apply an algorithm that generates sentences based on the latest information. Furthermore, in the case of an expert's opinion, the generation unit can apply an algorithm that generates sentences based on reliable information. For example, when generating an expert's opinion, the generation unit can apply an algorithm that generates sentences based on reliable information. Furthermore, in the case of a social media post, the generation unit can apply an algorithm that generates sentences based on a user's interests. For example, when generating a social media post, the generation unit can apply an algorithm that generates sentences based on a user's interests. In this way, by applying different generation algorithms depending on the content of the sentence to be generated, more appropriate sentences can be provided.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The reception section accepts input of the topic and content that the commentator wants to write about. For example, a commentator can input a topic such as "I want to comment on the latest news." Step 2: The collection unit collects related information based on the themes and content received by the reception unit. For example, the collection unit collects news articles and expert opinions from the Internet. Step 3: The generator generates an outline for writing based on the collected information. For example, the generator generates an outline such as "Major topics in the latest news." Step 4: The generator generates a specific sentence based on the generated outline. For example, the generator generates a sentence such as "Write the following comment about the latest news." Step 5: The verification section performs a final check of the generated text and makes corrections as necessary. For example, the verification section can review the generated text and make corrections such as "I would like to write this part in more detail."

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception section that accepts input of the topic and content you want to write about, a collection unit that collects related information based on the theme or content received by the reception unit; a generating unit that generates an outline for writing based on the information collected by the collecting unit; a unit for generating a specific sentence based on the outline generated by the generation unit; a confirmation unit that performs final confirmation of the sentence generated by the generation unit. A system characterized by:

2. The collecting unit Gather relevant information from news articles or expert opinions on the Internet 2. The system of claim 1.

3. The generation unit Generate an outline for your writing based on the information gathered 2. The system of claim 1.

4. The generation unit Generate specific sentences based on the generated outline 2. The system of claim 1.

5. The confirmation unit Check the generated text for the final time and make any necessary corrections.

2. The system of claim 1.

6. The reception unit Inferring user emotions and adjusting the topic and content input method based on the inferred emotions 2. The system of claim 1.

7. The reception unit Analyzes the user's past input history and suggests appropriate input formats 2. The system of claim 1.

8. The reception unit As you type topics and content, suggestions are provided based on your current interests and trends.

2. The system of claim 1.

9. The reception unit When entering a topic or content, select the most appropriate input method according to the user's input method.

2. The system of claim 1.

10. The reception unit Estimate the user's emotions and prioritize input content based on the estimated emotions 2. The system of claim 1.

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